我創(chuàng)建了一個自定義 Keras 層。該模型編譯得很好，但在訓練時給了我以下錯誤：ValueError：一個操作有None梯度。請確保您的所有操作都定義了漸變（即可微分）。沒有梯度的常見操作：K.argmax、K.round、K.eval。我的自定義層中是否有任何實現(xiàn)錯誤？class SpatialLayer(Layer):    def __init__(self, output_dim, **kwargs):        self.output_dim = output_dim        super(SpatialLayer, self).__init__(**kwargs)    def build(self, input_shape):        self.bias = None        self.built = True        self.kernelA = self.add_weight(name='kernelA', shape=(input_shape[1]-2, self.output_dim), initializer='uniform', trainable=True)    def compute_output_shape(self, input_shape):        return (input_shape[0], input_shape[1]-2, input_shape[1]-2, self.output_dim)    def call(self, inputs):        x_shape = tf.shape(inputs)        top_values, top_indices = tf.nn.top_k(tf.reshape(inputs, (-1,)), 10, sorted=True,)        top_indices = tf.stack(((top_indices // x_shape[1]), (top_indices % x_shape[1])), -1)        top_indices = tf.cast(top_indices, dtype=tf.float32)        t1 = tf.reshape(top_indices, (1,10,2))        t2 = tf.reshape(top_indices, (10,1,2))        result = tf.norm(t1-t2, ord='euclidean', axis=2)        x = tf.placeholder(tf.float32, shape=[None, 10, 10, 1])        tensor_zeros = tf.zeros_like(x)        matrix = tensor_zeros + result        return K.dot(matrix, self.kernelA)    model = applications.VGG16(weights = "imagenet", include_top=False, input_shape = (img_width, img_height, 3))    model.layers.pop()    new_custom_layers = model.layers[-1].output    model.layers[-1].trainable = False    new_custom_layers = Conv2D(filters=1, kernel_size=(3, 3))(new_custom_layers)    new_custom_layers = SpatialLayer(output_dim=1)(new_custom_layers)    new_custom_layers = Flatten()(new_custom_layers)    new_custom_layers = Dense(1024, activation="relu")(new_custom_layers)    new_custom_layers = Dropout(0.5)(new_custom_layers)    new_custom_layers = Dense(1024, activation="relu")(new_custom_layers)任何幫助，將不勝感激。